A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers

被引：0
作者：
Leticia Amador-Angulo
Oscar Castillo
机构：
[1] Tijuana Institute of Technology,Division of Graduate Studies
来源：
Soft Computing | 2018年 / 22卷
关键词：
Interval type-2 fuzzy logic system; Fuzzy controller; Bio-inspired algorithm; Footprint uncertainty; Bee colony optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents a new fuzzy bee colony optimization method to find the optimal distribution of the membership functions in the design of fuzzy controllers for complex nonlinear plants. We used interval type-2 and type-1 fuzzy logic systems in dynamically adapting the alpha and beta parameter values of the bee colony optimization algorithm (BCO). Simulation results with a type-1 fuzzy logic controller for benchmark control plants are presented. The advantage of using interval type-2 fuzzy logic systems for dynamic adjustment of parameters in BCO applied in fuzzy controller design is verified with two benchmark problems. We considered different levels and types of noise in the simulations to analyze the approach of interval type-2 fuzzy logic systems to find the best values of alpha and beta for BCO when applied in the design of fuzzy controllers.
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页码：571 / 594
页数：23
相关论文
共 90 条
[1]  
Arabshahi P(1996)Fuzzy parameter adaptation in optimization: some neural net training examples Comput Sci Eng 1 57-65
[2]  
Choi JJ(2007)A proposal of fuzzy control systems for trailers driven by multiple motors in side slipways to haul out ships Int J Innov Comput Inf Control 3 799-812
[3]  
Marks RJ(2013)Multiobjective optimization and comparison of nonsingleton type-1 and singleton interval type-2 fuzzy logic systems IEEE Trans Fuzzy Syst 21 459-476
[4]  
Caudell TP(2005)The use of waggle dance information by honey bees throughout their foraging careers Behav Ecol Sociobiol 59 133-142
[5]  
Aung N(2012)A review on the design and optimization of interval type-2 fuzzy controllers Appl Soft Comput (ASC) 12 1267-1278
[6]  
Cooper E(2012)Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot Inf Sci 192 19-38
[7]  
Hoshino Y(2012)Designing type-1 and type-2 fuzzy logic controllers via fuzzy Lyapunov synthesis for nonsmooth mechanical systems Eng Appl Artif Intell 25 971-979
[8]  
Kamei K(2012)A bee colony optimization based-fuzzy logic-PID control design of electrolyzer for microgrid stabilization Int J Innov Comput Inf Control 8 6049-6066
[9]  
Bel A(2006)Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization Comput Oper Res 33 859-871
[10]  
Wagner C(2013)Fuzzy rules interpolation for sparse fuzzy rule-based systems based on interval type-2 gaussian fuzzy sets and genetic algorithms IEEE Trans Fuzzy Syst 21 412-425
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